



import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler







class 				_a				:

			def __init__(self,   SCREAMING_SNAKE_CASE_					)       ->   str:
						if isinstance(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					):
									# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
									# modified it, it will not be accepted here again, since `auto` values would have been overridden
									UpperCAmelCase_:		Optional[Any]							      =     deepcopy(SCREAMING_SNAKE_CASE_					)
						elif os.path.exists(SCREAMING_SNAKE_CASE_					):
									with io.open(SCREAMING_SNAKE_CASE_,   """r""",   encoding="""utf-8"""					) as f:
												UpperCAmelCase_:		Union[str, Any]							      =     json.load(SCREAMING_SNAKE_CASE_					)
						else:
									try:
												UpperCAmelCase_:		str							      =     baseaa.urlsafe_baadecode(SCREAMING_SNAKE_CASE_					).decode("""utf-8"""					)
												UpperCAmelCase_:		Any							      =     json.loads(SCREAMING_SNAKE_CASE_					)
									except (UnicodeDecodeError, AttributeError, ValueError):
												raise ValueError(
												    f'Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'					)

						UpperCAmelCase_:		Any							      =     config

						self.set_stage_and_offload()

			def      __snake_case       (self					)       ->   Union[str, Any]:
						# zero stage - this is done as early as possible, before model is created, to allow
						# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
						# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
						UpperCAmelCase_:		Tuple							      =     self.get_value("""zero_optimization.stage""",   -1					)

						# offload
						UpperCAmelCase_:		Union[str, Any]							      =     False
						if self.is_zeroa() or self.is_zeroa():
									UpperCAmelCase_:		List[Any]							      =     set(["""cpu""", """nvme"""]					)
									UpperCAmelCase_:		List[Any]							      =     set(
									    [
									        self.get_value("""zero_optimization.offload_optimizer.device"""					),
									        self.get_value("""zero_optimization.offload_param.device"""					),
									    ]					)
									if len(offload_devices & offload_devices_valid					) > 0:
												UpperCAmelCase_:		List[str]							      =     True

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   int:
						UpperCAmelCase_:		Optional[int]							      =     self.config

						# find the config node of interest if it exists
						UpperCAmelCase_:		Dict							      =     ds_key_long.split("""."""					)
						UpperCAmelCase_:		Tuple							      =     nodes.pop()
						for node in nodes:
									UpperCAmelCase_:		Optional[int]							      =     config.get(SCREAMING_SNAKE_CASE_					)
									if config is None:
												return None, ds_key

						return config, ds_key

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=None					)       ->   Optional[Any]:
						UpperCAmelCase_      ,							UpperCAmelCase_:		Union[str, Any]							      =     self.find_config_node(SCREAMING_SNAKE_CASE_					)
						if config is None:
									return default
						return config.get(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=False					)       ->   List[str]:
						UpperCAmelCase_:		str							      =     self.config

						# find the config node of interest if it exists
						UpperCAmelCase_:		Optional[Any]							      =     ds_key_long.split("""."""					)
						for node in nodes:
									UpperCAmelCase_:		Optional[int]							      =     config
									UpperCAmelCase_:		int							      =     config.get(SCREAMING_SNAKE_CASE_					)
									if config is None:
												if must_exist:
															raise ValueError(f'Can\'t find {ds_key_long} entry in the config: {self.config}'					)
												else:
															return

        # if found remove it
						if parent_config is not None:
									parent_config.pop(SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Any:
						UpperCAmelCase_:		List[Any]							      =     self.get_value(SCREAMING_SNAKE_CASE_					)
						return False if value is None else bool(SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						UpperCAmelCase_:		List[Any]							      =     self.get_value(SCREAMING_SNAKE_CASE_					)
						return False if value is None else not bool(SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[Any]:
						return self._stage == 2

			def      __snake_case       (self					)       ->   Optional[int]:
						return self._stage == 3

			def      __snake_case       (self					)       ->   Dict:
						return self._offload







class 				_a				:

			def __init__(self,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		Optional[int]							      =     engine

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)       ->   List[str]:
						# runs backpropagation and handles mixed precision
						self.engine.backward(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

						# Deepspeed's `engine.step` performs the following operations:
						# - gradient accumulation check
						# - gradient clipping
						# - optimizer step
						# - zero grad
						# - checking overflow
						# - lr_scheduler step (only if engine.lr_scheduler is not None)
						self.engine.step()
						# and this plugin overrides the above calls with no-ops when Accelerate runs under
						# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
						# training loop that works transparently under many training regimes.







class 				_a				(   _lowerCAmelCase					):

			def __init__(self,   SCREAMING_SNAKE_CASE_					)       ->   str:
						super().__init__(SCREAMING_SNAKE_CASE_,   device_placement=SCREAMING_SNAKE_CASE_,   scaler=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     hasattr(self.optimizer,   """overflow"""					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_=None					)       ->   Optional[Any]:
						pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

			def      __snake_case       (self					)       ->   int:
						pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

			@property
			def      __snake_case       (self					)       ->   Dict:
						if self.__has_overflow__:
									return self.optimizer.overflow
						return False







class 				_a				(   _lowerCAmelCase					):

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Union[str, Any]:
						super().__init__(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[Any]:
						pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







class 				_a				:

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=0.0_0_1,   SCREAMING_SNAKE_CASE_=0,   **SCREAMING_SNAKE_CASE_					)       ->   Optional[int]:
						UpperCAmelCase_:		Any							      =     params
						UpperCAmelCase_:		Any							      =     lr
						UpperCAmelCase_:		int							      =     weight_decay
						UpperCAmelCase_:		Tuple							      =     kwargs







class 				_a				:

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=0,   **SCREAMING_SNAKE_CASE_					)       ->   Optional[Any]:
						UpperCAmelCase_:		Union[str, Any]							      =     optimizer
						UpperCAmelCase_:		List[str]							      =     total_num_steps
						UpperCAmelCase_:		Union[str, Any]							      =     warmup_num_steps
						UpperCAmelCase_:		Optional[Any]							      =     kwargs











import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a      :  Optional[int]			     =			logging.getLogger(__name__)







@dataclass
class 				_a				(   _lowerCAmelCase					):
			A      		=	field(
			    default=0.0 ,		metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''whether to use adafactor'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default='''linear''' ,		metadata={'''help''': F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}'''} ,		)






